This section introduces aspects that may be helpful in facilitating a better understanding of the systems and methods disclosed herein. Accordingly, the statements of this section are to be read in this light and are not to be understood or interpreted as admissions about what is or is not in the prior art.
Digital image/video cameras acquire and process a significant amount of raw data that is reduced using compression. In conventional cameras, raw data for each of an N-pixel image representing a scene is first captured and then typically compressed using a suitable compression algorithm for storage and/or transmission. Although compression after capturing a high resolution N-pixel image is generally useful, it requires significant computational resources and time. Furthermore, compression of the image raw data after it is acquired does not always result in meaningful compression.
A more recent approach, known in the art as compressive sense imaging, directly acquires compressed data for an N-pixel image (or images in case of video) of a scene. Compressive sense imaging is implemented using algorithms that use random or sparse projections to directly generate compressed measurements for later constructing the N-pixel image of the scene without collecting the conventional raw data of the image itself. Since a reduced number of compressive measurements are directly acquired in comparison to the more conventional method of first acquiring the raw data for each of the N-pixel values of a desired N-pixel image and then compressing the raw data, compressive sensing significantly eliminates or reduce resources needed for compressing an image after it is fully acquired. An N-pixel image of the scene is constructed from the compressed measurements for rendering on a display or other uses.